In this section, the detailed structure of the new proposed hybrid rule-neural actor-critic-based coreference system (HRN-ACC) is given. First we show how to generate the mention span representations in our system, which is inspired by the model given in [1]}.
Here a possible span is an N-gram within
a single document. We consider all possible spans
up to a predefined maximum width.
We will also explain the coreference rules used in our hybrid system, and the actor-critic structure for extracting the coreference entities from a document. Technical details are also given on training the hybrid rule-neural model model in semi-supervised fashion by maximizing the expected rewards using the actor-critic deep reinforcement learning model, as well as jointly training mention clustering and mention detection using the augmented actor-critic loss functions.
